Overview

Dataset statistics

Number of variables26
Number of observations2138470
Missing cells174
Missing cells (%)< 0.1%
Duplicate rows177287
Duplicate rows (%)8.3%
Total size in memory440.5 MiB
Average record size in memory216.0 B

Variable types

Categorical8
DateTime2
Numeric16

Alerts

Dataset has 177287 (8.3%) duplicate rowsDuplicates
VIN has a high cardinality: 1802040 distinct valuesHigh cardinality
MotorType has a high cardinality: 31271 distinct valuesHigh cardinality
Make has a high cardinality: 872 distinct valuesHigh cardinality
Model has a high cardinality: 17841 distinct valuesHigh cardinality
Type is highly imbalanced (79.2%)Imbalance
Make is highly imbalanced (56.5%)Imbalance
VehicleType is highly imbalanced (63.2%)Imbalance
VehicleClass is highly imbalanced (79.3%)Imbalance
Result is highly imbalanced (64.3%)Imbalance
Defects9 is highly skewed (γ1 = 42.94663245)Skewed
VIN is uniformly distributedUniform
DefectsA has 65350 (3.1%) zerosZeros
DefectsB has 1898477 (88.8%) zerosZeros
DefectsC has 2117777 (99.0%) zerosZeros
Defects0 has 1878598 (87.8%) zerosZeros
Defects1 has 981908 (45.9%) zerosZeros
Defects2 has 1804170 (84.4%) zerosZeros
Defects3 has 1787349 (83.6%) zerosZeros
Defects4 has 1218352 (57.0%) zerosZeros
Defects5 has 1519431 (71.1%) zerosZeros
Defects6 has 388675 (18.2%) zerosZeros
Defects7 has 2114636 (98.9%) zerosZeros
Defects8 has 2078743 (97.2%) zerosZeros
Defects9 has 2136216 (99.9%) zerosZeros

Reproduction

Analysis started2023-04-17 14:13:12.408011
Analysis finished2023-04-17 14:14:46.373147
Duration1 minute and 33.97 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Type
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
pravidelná
1861180 
opakovaná
 
146187
před registrací
 
80780
evidenční
 
39220
na žádost zákazníka
 
6506
Other values (8)
 
4597

Length

Max length37
Median length10
Mean length10.16443
Min length3

Characters and Unicode

Total characters21736328
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowpravidelná
2nd rowpravidelná
3rd rowpravidelná
4th rowpravidelná
5th rowpravidelná

Common Values

ValueCountFrequency (%)
pravidelná 1861180
87.0%
opakovaná 146187
 
6.8%
před registrací 80780
 
3.8%
evidenční 39220
 
1.8%
na žádost zákazníka 6506
 
0.3%
před registrací - opakovaná 3273
 
0.2%
před schválením tech. zp. 864
 
< 0.1%
silniční - opakovaná po DN 223
 
< 0.1%
silniční - opakovaná 142
 
< 0.1%
před schválením tech. zp. - opakovaná 45
 
< 0.1%
Other values (3) 50
 
< 0.1%

Length

2023-04-17T16:14:46.415366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 1861180
82.9%
opakovaná 149871
 
6.7%
před 84962
 
3.8%
registrací 84053
 
3.7%
evidenční 39220
 
1.7%
na 6506
 
0.3%
žádost 6506
 
0.3%
zákazníka 6506
 
0.3%
3684
 
0.2%
schválením 909
 
< 0.1%
Other values (7) 2679
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 2264516
10.4%
e 2110476
9.7%
n 2104188
9.7%
p 2097145
9.6%
v 2051180
9.4%
r 2029286
9.3%
á 2024995
9.3%
d 1991868
9.2%
i 1985183
9.1%
l 1862454
8.6%
Other values (20) 1215037
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21622693
99.5%
Space Separator 107606
 
0.5%
Dash Punctuation 3684
 
< 0.1%
Other Punctuation 1818
 
< 0.1%
Uppercase Letter 527
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2264516
10.5%
e 2110476
9.8%
n 2104188
9.7%
p 2097145
9.7%
v 2051180
9.5%
r 2029286
9.4%
á 2024995
9.4%
d 1991868
9.2%
i 1985183
9.2%
l 1862454
8.6%
Other values (13) 1101402
5.1%
Uppercase Letter
ValueCountFrequency (%)
D 250
47.4%
N 223
42.3%
A 27
 
5.1%
R 27
 
5.1%
Space Separator
ValueCountFrequency (%)
107606
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3684
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1818
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21623220
99.5%
Common 113108
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2264516
10.5%
e 2110476
9.8%
n 2104188
9.7%
p 2097145
9.7%
v 2051180
9.5%
r 2029286
9.4%
á 2024995
9.4%
d 1991868
9.2%
i 1985183
9.2%
l 1862454
8.6%
Other values (17) 1101929
5.1%
Common
ValueCountFrequency (%)
107606
95.1%
- 3684
 
3.3%
. 1818
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19449181
89.5%
None 2287147
 
10.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2264516
11.6%
e 2110476
10.9%
n 2104188
10.8%
p 2097145
10.8%
v 2051180
10.5%
r 2029286
10.4%
d 1991868
10.2%
i 1985183
10.2%
l 1862454
9.6%
o 306471
 
1.6%
Other values (15) 646414
 
3.3%
None
ValueCountFrequency (%)
á 2024995
88.5%
í 131076
 
5.7%
ř 84985
 
3.7%
č 39585
 
1.7%
ž 6506
 
0.3%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1802040
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
YV1SW796952464453
 
9
TMBAEF200P0733425
 
9
JSAEGC31W00148625
 
8
TMAD281UAHJ131974
 
8
XLRAE45GF0L367085
 
8
Other values (1802035)
2138428 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters36353990
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1497146 ?
Unique (%)70.0%

Sample

1st rowYV2RT60A3LB303685
2nd rowSARRDWBKN4D622908
3rd rowVF7CH6FZC39041829
4th rowWVWZZZ9NZ7Y199944
5th rowVF32CHFXF41920369

Common Values

ValueCountFrequency (%)
YV1SW796952464453 9
 
< 0.1%
TMBAEF200P0733425 9
 
< 0.1%
JSAEGC31W00148625 8
 
< 0.1%
TMAD281UAHJ131974 8
 
< 0.1%
XLRAE45GF0L367085 8
 
< 0.1%
TMBBA25J5A3139348 7
 
< 0.1%
WVWZZZ3BZXE238795 7
 
< 0.1%
VF3YCPMFC12083811 7
 
< 0.1%
VF7YCUMFC12317573 7
 
< 0.1%
WF0RXXGBFR2R05339 7
 
< 0.1%
Other values (1802030) 2138393
> 99.9%

Length

2023-04-17T16:14:46.534561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
yv1sw796952464453 9
 
< 0.1%
tmbaef200p0733425 9
 
< 0.1%
jsaegc31w00148625 8
 
< 0.1%
tmad281uahj131974 8
 
< 0.1%
xlrae45gf0l367085 8
 
< 0.1%
wf0hxxgbvhts22828 7
 
< 0.1%
wdc1648281a454026 7
 
< 0.1%
yv1ms765262207762 7
 
< 0.1%
vf7tdx8zacl516371 7
 
< 0.1%
tmbds63u649065160 7
 
< 0.1%
Other values (1802030) 2138393
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 3299616
 
9.1%
1 2835497
 
7.8%
2 2336790
 
6.4%
3 2116671
 
5.8%
6 1955090
 
5.4%
4 1904118
 
5.2%
5 1878679
 
5.2%
Z 1688816
 
4.6%
7 1686526
 
4.6%
8 1625336
 
4.5%
Other values (29) 15026851
41.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21082023
58.0%
Uppercase Letter 15271916
42.0%
Dash Punctuation 42
 
< 0.1%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 1688816
 
11.1%
B 1258297
 
8.2%
W 1113616
 
7.3%
F 1093239
 
7.2%
M 996994
 
6.5%
T 916460
 
6.0%
A 826991
 
5.4%
V 788541
 
5.2%
X 634660
 
4.2%
J 618121
 
4.0%
Other values (16) 5336181
34.9%
Decimal Number
ValueCountFrequency (%)
0 3299616
15.7%
1 2835497
13.4%
2 2336790
11.1%
3 2116671
10.0%
6 1955090
9.3%
4 1904118
9.0%
5 1878679
8.9%
7 1686526
8.0%
8 1625336
7.7%
9 1443700
6.8%
Other Punctuation
ValueCountFrequency (%)
/ 6
66.7%
. 3
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21082074
58.0%
Latin 15271916
42.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 1688816
 
11.1%
B 1258297
 
8.2%
W 1113616
 
7.3%
F 1093239
 
7.2%
M 996994
 
6.5%
T 916460
 
6.0%
A 826991
 
5.4%
V 788541
 
5.2%
X 634660
 
4.2%
J 618121
 
4.0%
Other values (16) 5336181
34.9%
Common
ValueCountFrequency (%)
0 3299616
15.7%
1 2835497
13.4%
2 2336790
11.1%
3 2116671
10.0%
6 1955090
9.3%
4 1904118
9.0%
5 1878679
8.9%
7 1686526
8.0%
8 1625336
7.7%
9 1443700
6.8%
Other values (3) 51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36353990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3299616
 
9.1%
1 2835497
 
7.8%
2 2336790
 
6.4%
3 2116671
 
5.8%
6 1955090
 
5.4%
4 1904118
 
5.2%
5 1878679
 
5.2%
Z 1688816
 
4.6%
7 1686526
 
4.6%
8 1625336
 
4.5%
Other values (29) 15026851
41.3%

Date
Date

Distinct313
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
Minimum2022-01-03 00:00:00
Maximum2022-11-30 00:00:00
2023-04-17T16:14:46.615162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:46.702749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MotorType
Categorical

Distinct31271
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
BXE
 
29587
ALH
 
29294
ASV
 
20684
AQW
 
19448
BLS
 
18628
Other values (31266)
2020829 

Length

Max length17
Median length16
Mean length4.542168
Min length1

Characters and Unicode

Total characters9713290
Distinct characters94
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13836 ?
Unique (%)0.6%

Sample

1st rowD13K540 EUVI
2nd row18K4F
3rd row6FZ
4th rowBWB
5th rowHFX

Common Values

ValueCountFrequency (%)
BXE 29587
 
1.4%
ALH 29294
 
1.4%
ASV 20684
 
1.0%
AQW 19448
 
0.9%
BLS 18628
 
0.9%
BME 17587
 
0.8%
AZQ 17296
 
0.8%
AGR 17238
 
0.8%
BKD 16649
 
0.8%
AWY 15900
 
0.7%
Other values (31261) 1936159
90.5%

Length

2023-04-17T16:14:46.793139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7 35495
 
1.4%
bxe 29588
 
1.2%
alh 29323
 
1.2%
d 21311
 
0.9%
asv 20695
 
0.8%
m 20533
 
0.8%
781.136 20090
 
0.8%
aqw 19517
 
0.8%
bls 18635
 
0.8%
bme 17588
 
0.7%
Other values (22768) 2240675
90.6%

Most occurring characters

ValueCountFrequency (%)
A 842133
 
8.7%
B 593880
 
6.1%
1 560083
 
5.8%
F 512415
 
5.3%
4 437753
 
4.5%
D 433780
 
4.5%
0 392207
 
4.0%
C 367713
 
3.8%
336042
 
3.5%
6 302815
 
3.1%
Other values (84) 4934469
50.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6079964
62.6%
Decimal Number 3077211
31.7%
Space Separator 336042
 
3.5%
Other Punctuation 161440
 
1.7%
Dash Punctuation 54296
 
0.6%
Math Symbol 1377
 
< 0.1%
Lowercase Letter 1018
 
< 0.1%
Open Punctuation 975
 
< 0.1%
Close Punctuation 964
 
< 0.1%
Modifier Symbol 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 842133
13.9%
B 593880
 
9.8%
F 512415
 
8.4%
D 433780
 
7.1%
C 367713
 
6.0%
H 296275
 
4.9%
E 285650
 
4.7%
M 269898
 
4.4%
K 232964
 
3.8%
X 221891
 
3.6%
Other values (26) 2023365
33.3%
Lowercase Letter
ValueCountFrequency (%)
a 110
 
10.8%
t 81
 
8.0%
b 79
 
7.8%
p 66
 
6.5%
o 62
 
6.1%
f 61
 
6.0%
d 59
 
5.8%
c 48
 
4.7%
l 46
 
4.5%
h 42
 
4.1%
Other values (19) 364
35.8%
Decimal Number
ValueCountFrequency (%)
1 560083
18.2%
4 437753
14.2%
0 392207
12.7%
6 302815
9.8%
2 286777
9.3%
3 245783
8.0%
7 241522
7.8%
8 240484
7.8%
9 224013
 
7.3%
5 145774
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 142636
88.4%
/ 12908
 
8.0%
* 3287
 
2.0%
, 2526
 
1.6%
? 65
 
< 0.1%
; 13
 
< 0.1%
: 3
 
< 0.1%
& 1
 
< 0.1%
\ 1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 1
33.3%
˙ 1
33.3%
¨ 1
33.3%
Math Symbol
ValueCountFrequency (%)
+ 1376
99.9%
| 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 963
99.9%
] 1
 
0.1%
Space Separator
ValueCountFrequency (%)
336042
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54296
100.0%
Open Punctuation
ValueCountFrequency (%)
( 975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6080982
62.6%
Common 3632308
37.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 842133
13.8%
B 593880
 
9.8%
F 512415
 
8.4%
D 433780
 
7.1%
C 367713
 
6.0%
H 296275
 
4.9%
E 285650
 
4.7%
M 269898
 
4.4%
K 232964
 
3.8%
X 221891
 
3.6%
Other values (55) 2024383
33.3%
Common
ValueCountFrequency (%)
1 560083
15.4%
4 437753
12.1%
0 392207
10.8%
336042
9.3%
6 302815
8.3%
2 286777
7.9%
3 245783
6.8%
7 241522
6.6%
8 240484
6.6%
9 224013
 
6.2%
Other values (19) 364829
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9712364
> 99.9%
None 925
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 842133
 
8.7%
B 593880
 
6.1%
1 560083
 
5.8%
F 512415
 
5.3%
4 437753
 
4.5%
D 433780
 
4.5%
0 392207
 
4.0%
C 367713
 
3.8%
336042
 
3.5%
6 302815
 
3.1%
Other values (68) 4933543
50.8%
None
ValueCountFrequency (%)
Š 818
88.4%
Ř 21
 
2.3%
Á 18
 
1.9%
Í 15
 
1.6%
Č 11
 
1.2%
Ý 11
 
1.2%
Ě 7
 
0.8%
š 7
 
0.8%
ý 5
 
0.5%
Ž 4
 
0.4%
Other values (5) 8
 
0.9%
Modifier Letters
ValueCountFrequency (%)
˙ 1
100.0%

Make
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct872
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
ŠKODA
608315 
FORD
177444 
VOLKSWAGEN
166320 
PEUGEOT
118342 
RENAULT
118221 
Other values (867)
949828 

Length

Max length28
Median length27
Mean length5.7617296
Min length2

Characters and Unicode

Total characters12321286
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique278 ?
Unique (%)< 0.1%

Sample

1st rowVOLVO
2nd rowMG
3rd rowCITROËN
4th rowVW
5th rowPEUGEOT

Common Values

ValueCountFrequency (%)
ŠKODA 608315
28.4%
FORD 177444
 
8.3%
VOLKSWAGEN 166320
 
7.8%
PEUGEOT 118342
 
5.5%
RENAULT 118221
 
5.5%
CITROËN 90396
 
4.2%
VW 81005
 
3.8%
OPEL 69314
 
3.2%
MERCEDES-BENZ 64793
 
3.0%
FIAT 58171
 
2.7%
Other values (862) 586149
27.4%

Length

2023-04-17T16:14:46.883278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 608349
28.2%
ford 177444
 
8.2%
volkswagen 166321
 
7.7%
peugeot 118342
 
5.5%
renault 118273
 
5.5%
citroën 90396
 
4.2%
vw 81005
 
3.8%
opel 69315
 
3.2%
mercedes-benz 64794
 
3.0%
fiat 58179
 
2.7%
Other values (882) 602442
28.0%

Most occurring characters

ValueCountFrequency (%)
O 1483946
 
12.0%
A 1443827
 
11.7%
D 1046444
 
8.5%
E 980656
 
8.0%
K 835959
 
6.8%
N 613408
 
5.0%
Š 608360
 
4.9%
T 557651
 
4.5%
R 527502
 
4.3%
I 462473
 
3.8%
Other values (59) 3761060
30.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12238902
99.3%
Dash Punctuation 65501
 
0.5%
Space Separator 16396
 
0.1%
Other Punctuation 376
 
< 0.1%
Decimal Number 80
 
< 0.1%
Lowercase Letter 23
 
< 0.1%
Math Symbol 4
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1483946
12.1%
A 1443827
 
11.8%
D 1046444
 
8.6%
E 980656
 
8.0%
K 835959
 
6.8%
N 613408
 
5.0%
Š 608360
 
5.0%
T 557651
 
4.6%
R 527502
 
4.3%
I 462473
 
3.8%
Other values (26) 3678676
30.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
21.7%
s 4
17.4%
n 2
 
8.7%
r 2
 
8.7%
ü 1
 
4.3%
l 1
 
4.3%
p 1
 
4.3%
u 1
 
4.3%
b 1
 
4.3%
z 1
 
4.3%
Other values (4) 4
17.4%
Decimal Number
ValueCountFrequency (%)
0 20
25.0%
3 20
25.0%
5 14
17.5%
8 9
11.2%
1 8
 
10.0%
4 5
 
6.2%
2 2
 
2.5%
6 1
 
1.2%
7 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 245
65.2%
/ 128
34.0%
, 1
 
0.3%
" 1
 
0.3%
& 1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 65501
100.0%
Space Separator
ValueCountFrequency (%)
16396
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12238925
99.3%
Common 82361
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1483946
12.1%
A 1443827
 
11.8%
D 1046444
 
8.6%
E 980656
 
8.0%
K 835959
 
6.8%
N 613408
 
5.0%
Š 608360
 
5.0%
T 557651
 
4.6%
R 527502
 
4.3%
I 462473
 
3.8%
Other values (40) 3678699
30.1%
Common
ValueCountFrequency (%)
- 65501
79.5%
16396
 
19.9%
. 245
 
0.3%
/ 128
 
0.2%
0 20
 
< 0.1%
3 20
 
< 0.1%
5 14
 
< 0.1%
8 9
 
< 0.1%
1 8
 
< 0.1%
4 5
 
< 0.1%
Other values (9) 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11622325
94.3%
None 698961
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1483946
12.8%
A 1443827
12.4%
D 1046444
 
9.0%
E 980656
 
8.4%
K 835959
 
7.2%
N 613408
 
5.3%
T 557651
 
4.8%
R 527502
 
4.5%
I 462473
 
4.0%
U 428192
 
3.7%
Other values (48) 3242267
27.9%
None
ValueCountFrequency (%)
Š 608360
87.0%
Ë 90396
 
12.9%
Ü 73
 
< 0.1%
Č 44
 
< 0.1%
Ö 30
 
< 0.1%
Á 25
 
< 0.1%
Ě 14
 
< 0.1%
Ž 14
 
< 0.1%
Í 3
 
< 0.1%
ü 1
 
< 0.1%

VehicleType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
OSOBNÍ AUTOMOBIL
1773081 
NÁKLADNÍ AUTOMOBIL
338647 
AUTOBUS
 
14156
MOTOCYKL
 
12586

Length

Max length18
Median length16
Mean length16.210058
Min length7

Characters and Unicode

Total characters34664722
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNÁKLADNÍ AUTOMOBIL
2nd rowOSOBNÍ AUTOMOBIL
3rd rowOSOBNÍ AUTOMOBIL
4th rowOSOBNÍ AUTOMOBIL
5th rowOSOBNÍ AUTOMOBIL

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 1773081
82.9%
NÁKLADNÍ AUTOMOBIL 338647
 
15.8%
AUTOBUS 14156
 
0.7%
MOTOCYKL 12586
 
0.6%

Length

2023-04-17T16:14:46.959081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:14:47.050635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
automobil 2111728
49.7%
osobní 1773081
41.7%
nákladní 338647
 
8.0%
autobus 14156
 
0.3%
motocykl 12586
 
0.3%

Most occurring characters

ValueCountFrequency (%)
O 7808946
22.5%
B 3898965
11.2%
A 2464531
 
7.1%
L 2462961
 
7.1%
N 2450375
 
7.1%
U 2140040
 
6.2%
T 2138470
 
6.2%
M 2124314
 
6.1%
2111728
 
6.1%
I 2111728
 
6.1%
Other values (7) 4952664
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 32552994
93.9%
Space Separator 2111728
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 7808946
24.0%
B 3898965
12.0%
A 2464531
 
7.6%
L 2462961
 
7.6%
N 2450375
 
7.5%
U 2140040
 
6.6%
T 2138470
 
6.6%
M 2124314
 
6.5%
I 2111728
 
6.5%
Í 2111728
 
6.5%
Other values (6) 2840936
 
8.7%
Space Separator
ValueCountFrequency (%)
2111728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32552994
93.9%
Common 2111728
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 7808946
24.0%
B 3898965
12.0%
A 2464531
 
7.6%
L 2462961
 
7.6%
N 2450375
 
7.5%
U 2140040
 
6.6%
T 2138470
 
6.6%
M 2124314
 
6.5%
I 2111728
 
6.5%
Í 2111728
 
6.5%
Other values (6) 2840936
 
8.7%
Common
ValueCountFrequency (%)
2111728
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32214347
92.9%
None 2450375
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 7808946
24.2%
B 3898965
12.1%
A 2464531
 
7.7%
L 2462961
 
7.6%
N 2450375
 
7.6%
U 2140040
 
6.6%
T 2138470
 
6.6%
M 2124314
 
6.6%
2111728
 
6.6%
I 2111728
 
6.6%
Other values (5) 2502289
 
7.8%
None
ValueCountFrequency (%)
Í 2111728
86.2%
Á 338647
 
13.8%

Model
Categorical

Distinct17841
Distinct (%)0.8%
Missing174
Missing (%)< 0.1%
Memory size32.6 MiB
OCTAVIA
220509 
FABIA
181004 
GOLF
 
49570
FOCUS
 
46220
FABIA COMBI
 
42583
Other values (17836)
1598410 

Length

Max length30
Median length28
Mean length6.2008043
Min length1

Characters and Unicode

Total characters13259155
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7911 ?
Unique (%)0.4%

Sample

1st rowFH
2nd rowMG TF
3rd rowXSARA PICASSO
4th rowPOLO
5th row206

Common Values

ValueCountFrequency (%)
OCTAVIA 220509
 
10.3%
FABIA 181004
 
8.5%
GOLF 49570
 
2.3%
FOCUS 46220
 
2.2%
FABIA COMBI 42583
 
2.0%
FELICIA 37827
 
1.8%
OCTAVIA COMBI 30535
 
1.4%
PASSAT 30330
 
1.4%
SUPERB 28033
 
1.3%
TRANSIT 27018
 
1.3%
Other values (17831) 1444667
67.6%

Length

2023-04-17T16:14:47.137465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 251151
 
9.9%
fabia 226287
 
8.9%
combi 81083
 
3.2%
golf 62364
 
2.5%
passat 51959
 
2.1%
focus 51621
 
2.0%
felicia 44717
 
1.8%
megane 38663
 
1.5%
transit 32593
 
1.3%
superb 29220
 
1.2%
Other values (10888) 1663483
65.7%

Most occurring characters

ValueCountFrequency (%)
A 2083526
15.7%
I 1113514
 
8.4%
O 1027808
 
7.8%
T 854398
 
6.4%
C 817777
 
6.2%
R 718159
 
5.4%
E 630775
 
4.8%
S 617185
 
4.7%
F 503161
 
3.8%
N 471743
 
3.6%
Other values (71) 4421109
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11736445
88.5%
Decimal Number 1095607
 
8.3%
Space Separator 395063
 
3.0%
Lowercase Letter 32034
 
0.2%
Connector Punctuation 5
 
< 0.1%
Modifier Letter 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2083526
17.8%
I 1113514
 
9.5%
O 1027808
 
8.8%
T 854398
 
7.3%
C 817777
 
7.0%
R 718159
 
6.1%
E 630775
 
5.4%
S 617185
 
5.3%
F 503161
 
4.3%
N 471743
 
4.0%
Other values (27) 2898399
24.7%
Lowercase Letter
ValueCountFrequency (%)
i 19711
61.5%
r 2042
 
6.4%
a 1995
 
6.2%
o 1868
 
5.8%
x 1094
 
3.4%
e 927
 
2.9%
t 616
 
1.9%
d 603
 
1.9%
n 601
 
1.9%
s 527
 
1.6%
Other values (21) 2050
 
6.4%
Decimal Number
ValueCountFrequency (%)
0 285094
26.0%
3 149001
13.6%
2 138022
12.6%
1 103332
 
9.4%
5 100634
 
9.2%
4 92078
 
8.4%
6 88413
 
8.1%
7 66046
 
6.0%
8 53780
 
4.9%
9 19207
 
1.8%
Space Separator
ValueCountFrequency (%)
395063
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Modifier Letter
ValueCountFrequency (%)
ˇ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11768479
88.8%
Common 1490676
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2083526
17.7%
I 1113514
 
9.5%
O 1027808
 
8.7%
T 854398
 
7.3%
C 817777
 
6.9%
R 718159
 
6.1%
E 630775
 
5.4%
S 617185
 
5.2%
F 503161
 
4.3%
N 471743
 
4.0%
Other values (58) 2930433
24.9%
Common
ValueCountFrequency (%)
395063
26.5%
0 285094
19.1%
3 149001
 
10.0%
2 138022
 
9.3%
1 103332
 
6.9%
5 100634
 
6.8%
4 92078
 
6.2%
6 88413
 
5.9%
7 66046
 
4.4%
8 53780
 
3.6%
Other values (3) 19213
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13258147
> 99.9%
None 1007
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2083526
15.7%
I 1113514
 
8.4%
O 1027808
 
7.8%
T 854398
 
6.4%
C 817777
 
6.2%
R 718159
 
5.4%
E 630775
 
4.8%
S 617185
 
4.7%
F 503161
 
3.8%
N 471743
 
3.6%
Other values (54) 4420101
33.3%
None
ValueCountFrequency (%)
É 520
51.6%
Á 336
33.4%
Ó 41
 
4.1%
á 26
 
2.6%
í 24
 
2.4%
é 12
 
1.2%
Ö 11
 
1.1%
Š 11
 
1.1%
Ü 10
 
1.0%
Č 6
 
0.6%
Other values (6) 10
 
1.0%
Modifier Letters
ValueCountFrequency (%)
ˇ 1
100.0%

VehicleClass
Categorical

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
M1
1729398 
N1
213619 
N3
 
56011
M1G
 
43683
N2
 
38027
Other values (42)
 
57732

Length

Max length7
Median length2
Mean length2.0384083
Min length1

Characters and Unicode

Total characters4359075
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowN3
2nd rowM1
3rd rowM1
4th rowM1
5th rowM1

Common Values

ValueCountFrequency (%)
M1 1729398
80.9%
N1 213619
 
10.0%
N3 56011
 
2.6%
M1G 43683
 
2.0%
N2 38027
 
1.8%
N1G 18179
 
0.9%
M3 13020
 
0.6%
N3G 12124
 
0.6%
LC 4833
 
0.2%
L3e 3450
 
0.2%
Other values (37) 6126
 
0.3%

Length

2023-04-17T16:14:47.216331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 1729398
80.9%
n1 213619
 
10.0%
n3 56011
 
2.6%
m1g 43683
 
2.0%
n2 38027
 
1.8%
n1g 18179
 
0.9%
m3 13020
 
0.6%
n3g 12124
 
0.6%
lc 4833
 
0.2%
l3e 3450
 
0.2%
Other values (37) 6126
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 2005773
46.0%
M 1787237
41.0%
N 338647
 
7.8%
3 85349
 
2.0%
G 74676
 
1.7%
2 39991
 
0.9%
L 12582
 
0.3%
e 5364
 
0.1%
C 4835
 
0.1%
A 1945
 
< 0.1%
Other values (13) 2676
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2221115
51.0%
Decimal Number 2131900
48.9%
Lowercase Letter 5365
 
0.1%
Dash Punctuation 695
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1787237
80.5%
N 338647
 
15.2%
G 74676
 
3.4%
L 12582
 
0.6%
C 4835
 
0.2%
A 1945
 
0.1%
E 887
 
< 0.1%
B 275
 
< 0.1%
P 23
 
< 0.1%
Z 4
 
< 0.1%
Other values (3) 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 2005773
94.1%
3 85349
 
4.0%
2 39991
 
1.9%
7 584
 
< 0.1%
6 147
 
< 0.1%
5 49
 
< 0.1%
4 7
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 5364
> 99.9%
z 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 695
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2226480
51.1%
Common 2132595
48.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1787237
80.3%
N 338647
 
15.2%
G 74676
 
3.4%
L 12582
 
0.6%
e 5364
 
0.2%
C 4835
 
0.2%
A 1945
 
0.1%
E 887
 
< 0.1%
B 275
 
< 0.1%
P 23
 
< 0.1%
Other values (5) 9
 
< 0.1%
Common
ValueCountFrequency (%)
1 2005773
94.1%
3 85349
 
4.0%
2 39991
 
1.9%
- 695
 
< 0.1%
7 584
 
< 0.1%
6 147
 
< 0.1%
5 49
 
< 0.1%
4 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4359075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2005773
46.0%
M 1787237
41.0%
N 338647
 
7.8%
3 85349
 
2.0%
G 74676
 
1.7%
2 39991
 
0.9%
L 12582
 
0.3%
e 5364
 
0.1%
C 4835
 
0.1%
A 1945
 
< 0.1%
Other values (13) 2676
 
0.1%
Distinct12664
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
Minimum1900-06-05 00:00:00
Maximum2022-11-29 00:00:00
2023-04-17T16:14:47.297003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:47.381887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct466805
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218863.99
Minimum0
Maximum999999
Zeros2441
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:47.478699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61908
Q1138672
median202158
Q3273588
95-th percentile422404
Maximum999999
Range999999
Interquartile range (IQR)134916

Descriptive statistics

Standard deviation124051.64
Coefficient of variation (CV)0.56679786
Kurtosis6.2543001
Mean218863.99
Median Absolute Deviation (MAD)67021
Skewness1.7893989
Sum4.6803408 × 1011
Variance1.538881 × 1010
MonotonicityNot monotonic
2023-04-17T16:14:47.570374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2441
 
0.1%
399999 116
 
< 0.1%
999999 56
 
< 0.1%
2 31
 
< 0.1%
5 29
 
< 0.1%
156336 28
 
< 0.1%
9 27
 
< 0.1%
1 26
 
< 0.1%
11 25
 
< 0.1%
10 25
 
< 0.1%
Other values (466795) 2135666
99.9%
ValueCountFrequency (%)
0 2441
0.1%
1 26
 
< 0.1%
2 31
 
< 0.1%
3 15
 
< 0.1%
4 23
 
< 0.1%
5 29
 
< 0.1%
6 19
 
< 0.1%
7 20
 
< 0.1%
8 20
 
< 0.1%
9 27
 
< 0.1%
ValueCountFrequency (%)
999999 56
< 0.1%
999998 1
 
< 0.1%
999996 1
 
< 0.1%
999989 1
 
< 0.1%
999981 1
 
< 0.1%
999874 1
 
< 0.1%
999764 1
 
< 0.1%
999722 1
 
< 0.1%
999688 1
 
< 0.1%
999674 1
 
< 0.1%

Result
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 MiB
způsobilé
1893321 
částečně způsobilé
222090 
nezpůsobilé
 
23059

Length

Max length18
Median length9
Mean length9.9562575
Min length9

Characters and Unicode

Total characters21291158
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowzpůsobilé
2nd rowzpůsobilé
3rd rowčástečně způsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 1893321
88.5%
částečně způsobilé 222090
 
10.4%
nezpůsobilé 23059
 
1.1%

Length

2023-04-17T16:14:47.644812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:14:47.719674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 2115411
89.6%
částečně 222090
 
9.4%
nezpůsobilé 23059
 
1.0%

Most occurring characters

ValueCountFrequency (%)
s 2360560
11.1%
z 2138470
10.0%
p 2138470
10.0%
ů 2138470
10.0%
o 2138470
10.0%
b 2138470
10.0%
i 2138470
10.0%
l 2138470
10.0%
é 2138470
10.0%
č 444180
 
2.1%
Other values (6) 1378658
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21069068
99.0%
Space Separator 222090
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2360560
11.2%
z 2138470
10.1%
p 2138470
10.1%
ů 2138470
10.1%
o 2138470
10.1%
b 2138470
10.1%
i 2138470
10.1%
l 2138470
10.1%
é 2138470
10.1%
č 444180
 
2.1%
Other values (5) 1156568
5.5%
Space Separator
ValueCountFrequency (%)
222090
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21069068
99.0%
Common 222090
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2360560
11.2%
z 2138470
10.1%
p 2138470
10.1%
ů 2138470
10.1%
o 2138470
10.1%
b 2138470
10.1%
i 2138470
10.1%
l 2138470
10.1%
é 2138470
10.1%
č 444180
 
2.1%
Other values (5) 1156568
5.5%
Common
ValueCountFrequency (%)
222090
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16125858
75.7%
None 5165300
 
24.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 2360560
14.6%
z 2138470
13.3%
p 2138470
13.3%
o 2138470
13.3%
b 2138470
13.3%
i 2138470
13.3%
l 2138470
13.3%
e 245149
 
1.5%
n 245149
 
1.5%
t 222090
 
1.4%
None
ValueCountFrequency (%)
ů 2138470
41.4%
é 2138470
41.4%
č 444180
 
8.6%
á 222090
 
4.3%
ě 222090
 
4.3%

Weekday
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.899422
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:47.776608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3687662
Coefficient of variation (CV)0.47208243
Kurtosis-1.0958621
Mean2.899422
Median Absolute Deviation (MAD)1
Skewness0.13368228
Sum6200327
Variance1.8735209
MonotonicityNot monotonic
2023-04-17T16:14:47.830949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 479628
22.4%
2 467759
21.9%
4 431633
20.2%
1 428999
20.1%
5 312418
14.6%
6 17927
 
0.8%
7 106
 
< 0.1%
ValueCountFrequency (%)
1 428999
20.1%
2 467759
21.9%
3 479628
22.4%
4 431633
20.2%
5 312418
14.6%
6 17927
 
0.8%
7 106
 
< 0.1%
ValueCountFrequency (%)
7 106
 
< 0.1%
6 17927
 
0.8%
5 312418
14.6%
4 431633
20.2%
3 479628
22.4%
2 467759
21.9%
1 428999
20.1%

DefectsA
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4859156
Minimum0
Maximum30
Zeros65350
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:47.905485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3523875
Coefficient of variation (CV)0.67482628
Kurtosis1.7247104
Mean3.4859156
Median Absolute Deviation (MAD)2
Skewness1.0611766
Sum7454526
Variance5.5337268
MonotonicityNot monotonic
2023-04-17T16:14:47.978969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 421922
19.7%
2 379617
17.8%
3 345243
16.1%
4 296427
13.9%
5 251888
11.8%
6 149976
 
7.0%
7 94177
 
4.4%
0 65350
 
3.1%
8 58216
 
2.7%
9 32431
 
1.5%
Other values (21) 43223
 
2.0%
ValueCountFrequency (%)
0 65350
 
3.1%
1 421922
19.7%
2 379617
17.8%
3 345243
16.1%
4 296427
13.9%
5 251888
11.8%
6 149976
 
7.0%
7 94177
 
4.4%
8 58216
 
2.7%
9 32431
 
1.5%
ValueCountFrequency (%)
30 2
 
< 0.1%
29 1
 
< 0.1%
28 9
 
< 0.1%
27 5
 
< 0.1%
26 4
 
< 0.1%
25 4
 
< 0.1%
24 6
 
< 0.1%
23 11
 
< 0.1%
22 31
< 0.1%
21 47
< 0.1%

DefectsB
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28734469
Minimum0
Maximum29
Zeros1898477
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.057243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0558563
Coefficient of variation (CV)3.6745288
Kurtosis42.144695
Mean0.28734469
Median Absolute Deviation (MAD)0
Skewness5.5155682
Sum614478
Variance1.1148326
MonotonicityNot monotonic
2023-04-17T16:14:48.132836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 1898477
88.8%
1 95759
 
4.5%
2 54407
 
2.5%
3 35179
 
1.6%
4 21520
 
1.0%
5 13030
 
0.6%
6 7990
 
0.4%
7 4585
 
0.2%
8 2800
 
0.1%
9 1712
 
0.1%
Other values (19) 3011
 
0.1%
ValueCountFrequency (%)
0 1898477
88.8%
1 95759
 
4.5%
2 54407
 
2.5%
3 35179
 
1.6%
4 21520
 
1.0%
5 13030
 
0.6%
6 7990
 
0.4%
7 4585
 
0.2%
8 2800
 
0.1%
9 1712
 
0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
27 2
 
< 0.1%
26 3
 
< 0.1%
25 1
 
< 0.1%
24 2
 
< 0.1%
23 4
 
< 0.1%
22 10
< 0.1%
21 10
< 0.1%
20 16
< 0.1%
19 17
< 0.1%

DefectsC
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013385271
Minimum0
Maximum14
Zeros2117777
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.202019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15525644
Coefficient of variation (CV)11.599051
Kurtosis450.4525
Mean0.013385271
Median Absolute Deviation (MAD)0
Skewness17.005798
Sum28624
Variance0.024104563
MonotonicityNot monotonic
2023-04-17T16:14:48.262631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 2117777
99.0%
1 15041
 
0.7%
2 4135
 
0.2%
3 1078
 
0.1%
4 279
 
< 0.1%
5 89
 
< 0.1%
6 30
 
< 0.1%
7 16
 
< 0.1%
8 11
 
< 0.1%
9 9
 
< 0.1%
Other values (4) 5
 
< 0.1%
ValueCountFrequency (%)
0 2117777
99.0%
1 15041
 
0.7%
2 4135
 
0.2%
3 1078
 
0.1%
4 279
 
< 0.1%
5 89
 
< 0.1%
6 30
 
< 0.1%
7 16
 
< 0.1%
8 11
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 9
 
< 0.1%
8 11
 
< 0.1%
7 16
 
< 0.1%
6 30
 
< 0.1%
5 89
 
< 0.1%
4 279
< 0.1%

Defects0
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12935323
Minimum0
Maximum6
Zeros1878598
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.327261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35876321
Coefficient of variation (CV)2.7735157
Kurtosis7.4340873
Mean0.12935323
Median Absolute Deviation (MAD)0
Skewness2.7500863
Sum276618
Variance0.12871104
MonotonicityNot monotonic
2023-04-17T16:14:48.385100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1878598
87.8%
1 243526
 
11.4%
2 15994
 
0.7%
3 313
 
< 0.1%
4 31
 
< 0.1%
5 7
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1878598
87.8%
1 243526
 
11.4%
2 15994
 
0.7%
3 313
 
< 0.1%
4 31
 
< 0.1%
5 7
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 7
 
< 0.1%
4 31
 
< 0.1%
3 313
 
< 0.1%
2 15994
 
0.7%
1 243526
 
11.4%
0 1878598
87.8%

Defects1
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82169729
Minimum0
Maximum17
Zeros981908
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.449739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95573956
Coefficient of variation (CV)1.1631285
Kurtosis2.5206279
Mean0.82169729
Median Absolute Deviation (MAD)1
Skewness1.3244243
Sum1757175
Variance0.9134381
MonotonicityNot monotonic
2023-04-17T16:14:48.515347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 981908
45.9%
1 719176
33.6%
2 314794
 
14.7%
3 92473
 
4.3%
4 22799
 
1.1%
5 5225
 
0.2%
6 1399
 
0.1%
7 448
 
< 0.1%
8 169
 
< 0.1%
9 43
 
< 0.1%
Other values (7) 36
 
< 0.1%
ValueCountFrequency (%)
0 981908
45.9%
1 719176
33.6%
2 314794
 
14.7%
3 92473
 
4.3%
4 22799
 
1.1%
5 5225
 
0.2%
6 1399
 
0.1%
7 448
 
< 0.1%
8 169
 
< 0.1%
9 43
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 10
 
< 0.1%
10 17
 
< 0.1%
9 43
 
< 0.1%
8 169
 
< 0.1%
7 448
< 0.1%

Defects2
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17047375
Minimum0
Maximum7
Zeros1804170
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.583021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41488418
Coefficient of variation (CV)2.433713
Kurtosis6.6860196
Mean0.17047375
Median Absolute Deviation (MAD)0
Skewness2.4901621
Sum364553
Variance0.17212888
MonotonicityNot monotonic
2023-04-17T16:14:48.643041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1804170
84.4%
1 306362
 
14.3%
2 25854
 
1.2%
3 1887
 
0.1%
4 172
 
< 0.1%
5 17
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1804170
84.4%
1 306362
 
14.3%
2 25854
 
1.2%
3 1887
 
0.1%
4 172
 
< 0.1%
5 17
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 7
 
< 0.1%
5 17
 
< 0.1%
4 172
 
< 0.1%
3 1887
 
0.1%
2 25854
 
1.2%
1 306362
 
14.3%
0 1804170
84.4%

Defects3
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18903609
Minimum0
Maximum7
Zeros1787349
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.706188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45968603
Coefficient of variation (CV)2.4317369
Kurtosis9.0814112
Mean0.18903609
Median Absolute Deviation (MAD)0
Skewness2.7318957
Sum404248
Variance0.21131124
MonotonicityNot monotonic
2023-04-17T16:14:48.766030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1787349
83.6%
1 305691
 
14.3%
2 38765
 
1.8%
3 5778
 
0.3%
4 765
 
< 0.1%
5 101
 
< 0.1%
6 19
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 1787349
83.6%
1 305691
 
14.3%
2 38765
 
1.8%
3 5778
 
0.3%
4 765
 
< 0.1%
5 101
 
< 0.1%
6 19
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 19
 
< 0.1%
5 101
 
< 0.1%
4 765
 
< 0.1%
3 5778
 
0.3%
2 38765
 
1.8%
1 305691
 
14.3%
0 1787349
83.6%

Defects4
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59487157
Minimum0
Maximum14
Zeros1218352
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.832005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83039335
Coefficient of variation (CV)1.3959204
Kurtosis5.72467
Mean0.59487157
Median Absolute Deviation (MAD)0
Skewness1.8209044
Sum1272115
Variance0.68955311
MonotonicityNot monotonic
2023-04-17T16:14:48.896331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1218352
57.0%
1 655271
30.6%
2 203008
 
9.5%
3 45601
 
2.1%
4 10731
 
0.5%
5 3387
 
0.2%
6 1285
 
0.1%
7 496
 
< 0.1%
8 187
 
< 0.1%
9 76
 
< 0.1%
Other values (5) 76
 
< 0.1%
ValueCountFrequency (%)
0 1218352
57.0%
1 655271
30.6%
2 203008
 
9.5%
3 45601
 
2.1%
4 10731
 
0.5%
5 3387
 
0.2%
6 1285
 
0.1%
7 496
 
< 0.1%
8 187
 
< 0.1%
9 76
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 2
 
< 0.1%
12 7
 
< 0.1%
11 20
 
< 0.1%
10 46
 
< 0.1%
9 76
 
< 0.1%
8 187
 
< 0.1%
7 496
 
< 0.1%
6 1285
 
0.1%
5 3387
0.2%

Defects5
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3463523
Minimum0
Maximum10
Zeros1519431
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:48.968719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60042324
Coefficient of variation (CV)1.7335622
Kurtosis4.7966399
Mean0.3463523
Median Absolute Deviation (MAD)0
Skewness1.9020501
Sum740664
Variance0.36050806
MonotonicityNot monotonic
2023-04-17T16:14:49.035715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1519431
71.1%
1 514394
 
24.1%
2 91259
 
4.3%
3 10686
 
0.5%
4 2024
 
0.1%
5 521
 
< 0.1%
6 110
 
< 0.1%
7 34
 
< 0.1%
8 6
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 1519431
71.1%
1 514394
 
24.1%
2 91259
 
4.3%
3 10686
 
0.5%
4 2024
 
0.1%
5 521
 
< 0.1%
6 110
 
< 0.1%
7 34
 
< 0.1%
8 6
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 3
 
< 0.1%
8 6
 
< 0.1%
7 34
 
< 0.1%
6 110
 
< 0.1%
5 521
 
< 0.1%
4 2024
 
0.1%
3 10686
 
0.5%
2 91259
 
4.3%
1 514394
24.1%

Defects6
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4760001
Minimum0
Maximum17
Zeros388675
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:49.107968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1298863
Coefficient of variation (CV)0.76550558
Kurtosis2.4659377
Mean1.4760001
Median Absolute Deviation (MAD)1
Skewness1.0456276
Sum3156382
Variance1.2766431
MonotonicityNot monotonic
2023-04-17T16:14:49.176713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 827160
38.7%
2 589328
27.6%
0 388675
18.2%
3 230755
 
10.8%
4 70909
 
3.3%
5 21325
 
1.0%
6 6600
 
0.3%
7 2339
 
0.1%
8 828
 
< 0.1%
9 300
 
< 0.1%
Other values (8) 251
 
< 0.1%
ValueCountFrequency (%)
0 388675
18.2%
1 827160
38.7%
2 589328
27.6%
3 230755
 
10.8%
4 70909
 
3.3%
5 21325
 
1.0%
6 6600
 
0.3%
7 2339
 
0.1%
8 828
 
< 0.1%
9 300
 
< 0.1%
ValueCountFrequency (%)
17 2
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 8
 
< 0.1%
13 18
 
< 0.1%
12 31
 
< 0.1%
11 49
 
< 0.1%
10 139
 
< 0.1%
9 300
 
< 0.1%
8 828
< 0.1%

Defects7
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012289628
Minimum0
Maximum6
Zeros2114636
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:49.245089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12142185
Coefficient of variation (CV)9.8800267
Kurtosis168.68993
Mean0.012289628
Median Absolute Deviation (MAD)0
Skewness11.566476
Sum26281
Variance0.014743266
MonotonicityNot monotonic
2023-04-17T16:14:49.303957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2114636
98.9%
1 21665
 
1.0%
2 1942
 
0.1%
3 183
 
< 0.1%
4 39
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 2114636
98.9%
1 21665
 
1.0%
2 1942
 
0.1%
3 183
 
< 0.1%
4 39
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 3
 
< 0.1%
4 39
 
< 0.1%
3 183
 
< 0.1%
2 1942
 
0.1%
1 21665
 
1.0%
0 2114636
98.9%

Defects8
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.045243562
Minimum0
Maximum8
Zeros2078743
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:49.369218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29732132
Coefficient of variation (CV)6.571572
Kurtosis73.682234
Mean0.045243562
Median Absolute Deviation (MAD)0
Skewness7.9250497
Sum96752
Variance0.08839997
MonotonicityNot monotonic
2023-04-17T16:14:49.433324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2078743
97.2%
1 31794
 
1.5%
2 20608
 
1.0%
3 5912
 
0.3%
4 1134
 
0.1%
5 222
 
< 0.1%
6 41
 
< 0.1%
7 14
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 2078743
97.2%
1 31794
 
1.5%
2 20608
 
1.0%
3 5912
 
0.3%
4 1134
 
0.1%
5 222
 
< 0.1%
6 41
 
< 0.1%
7 14
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 14
 
< 0.1%
6 41
 
< 0.1%
5 222
 
< 0.1%
4 1134
 
0.1%
3 5912
 
0.3%
2 20608
 
1.0%
1 31794
 
1.5%
0 2078743
97.2%

Defects9
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0013528364
Minimum0
Maximum5
Zeros2136216
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:49.499377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.045857896
Coefficient of variation (CV)33.897593
Kurtosis2296.3317
Mean0.0013528364
Median Absolute Deviation (MAD)0
Skewness42.946632
Sum2893
Variance0.0021029467
MonotonicityNot monotonic
2023-04-17T16:14:49.561594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2136216
99.9%
1 1753
 
0.1%
2 389
 
< 0.1%
3 87
 
< 0.1%
4 24
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 2136216
99.9%
1 1753
 
0.1%
2 389
 
< 0.1%
3 87
 
< 0.1%
4 24
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 24
 
< 0.1%
3 87
 
< 0.1%
2 389
 
< 0.1%
1 1753
 
0.1%
0 2136216
99.9%

AgeDays
Real number (ℝ)

Distinct12664
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6015.6094
Minimum139
Maximum44876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.6 MiB
2023-04-17T16:14:49.643469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum139
5-th percentile2408
Q14681
median6082
Q37448
95-th percentile9170
Maximum44876
Range44737
Interquartile range (IQR)2767

Descriptive statistics

Standard deviation2079.6851
Coefficient of variation (CV)0.34571478
Kurtosis0.59320868
Mean6015.6094
Median Absolute Deviation (MAD)1386
Skewness0.092472393
Sum1.28642 × 1010
Variance4325090.1
MonotonicityNot monotonic
2023-04-17T16:14:49.731745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8872 3640
 
0.2%
8507 3528
 
0.2%
9237 3395
 
0.2%
9602 3153
 
0.1%
9968 2315
 
0.1%
8141 1914
 
0.1%
10333 1683
 
0.1%
5495 1645
 
0.1%
5404 1588
 
0.1%
4674 1493
 
0.1%
Other values (12654) 2114116
98.9%
ValueCountFrequency (%)
139 2
 
< 0.1%
140 4
 
< 0.1%
143 2
 
< 0.1%
146 2
 
< 0.1%
147 4
 
< 0.1%
152 2
 
< 0.1%
153 2
 
< 0.1%
154 6
< 0.1%
157 4
 
< 0.1%
158 10
< 0.1%
ValueCountFrequency (%)
44876 2
< 0.1%
44863 2
< 0.1%
25308 2
< 0.1%
24943 2
< 0.1%
24138 1
 
< 0.1%
23450 1
 
< 0.1%
23117 2
< 0.1%
22751 4
< 0.1%
22725 1
 
< 0.1%
22709 1
 
< 0.1%

Interactions

2023-04-17T16:14:32.544216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:44.309754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:47.563715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:50.619087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:53.929877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:57.013304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:00.077594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:03.183816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:06.446469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:09.732008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:12.924209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:16.287952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:19.543963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:22.951814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:26.185659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:29.398068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:32.745459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:44.517484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:47.744031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:50.824002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:54.118994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:57.202008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:00.275640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:03.385768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:06.641884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:09.934859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:13.129931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:16.505073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:19.754217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:23.149106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:26.384050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:29.588206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:32.953289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:44.735780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:47.931830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:51.022106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:54.315204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:57.394654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:00.467937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:03.587920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:06.834687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:10.132859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:13.338479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:16.705903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:19.967636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:23.346266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:26.585245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:29.779887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:33.159659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:44.947302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:48.119954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:51.224637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:54.501496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:57.591937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:00.662155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:03.790251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:07.043227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:10.340277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:13.546669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:16.903624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:20.192223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:23.549223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:26.789019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:29.974725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:33.373122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:45.151513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:48.310750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:51.428962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:54.693585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:57.776255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:00.854053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:03.994929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:07.242089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:10.544605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:13.754683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:17.105346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:20.402984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:23.749331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:26.988949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:30.165818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:33.588993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:45.356779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:48.504463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:51.634672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:54.893550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:57.976970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:01.044838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:04.201713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:07.445975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:10.745351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:13.968058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:17.326361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:20.620548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:23.951097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:27.190296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:30.364548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:33.795838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:45.558680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:48.691022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:51.834257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:55.082433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:58.172270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:01.235575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:04.400950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:07.666628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:10.943373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:14.175668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:17.531869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:20.832864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:24.153251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:27.389943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:30.568495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:34.007715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:45.762595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:48.883419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:52.048797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:55.281582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:58.365251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:01.434828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:04.606826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:07.874205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:11.146410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:14.388307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:17.730517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:21.051406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:24.360998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:27.597114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:30.782159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:34.254340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:45.966069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:49.074297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:52.261304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:55.475934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:58.557933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:01.630702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:04.813382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:08.077298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:11.337384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:14.602610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:17.936195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:21.264913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:24.563411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:27.794924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:30.987612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:34.516031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:46.174020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:49.265890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:52.475161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:55.670362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:58.748949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:01.824693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:05.028267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:08.281940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:11.534029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:14.809952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:18.137909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:21.479083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:24.762070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:28.021443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:31.181128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:34.782819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:46.382035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:49.459891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:52.686666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:55.865019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:58.942518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:02.021387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:05.233556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:08.485727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:11.734542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:15.018718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:18.336820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:21.695650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:24.969143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:28.225625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:31.376373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:35.626375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:46.579223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:49.645830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:52.891517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:56.052796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:59.126063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:02.211851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:05.432914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:08.693783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:11.926992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:15.220482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:18.532941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:21.898109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:25.163603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:28.421583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:31.571978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:35.858108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:46.782975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:49.836741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:53.115422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:56.245905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:59.318597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:02.408062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:05.640666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:08.921289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:12.127311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:15.430630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:18.732338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:22.113290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:25.380610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:28.624896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:31.764838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:36.074840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:46.982503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:50.027403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:53.326587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:56.436649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:59.508735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:02.600961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:05.844851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:09.122293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:12.322046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:15.646138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:18.932961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:22.327043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:25.593733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:28.818581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:31.965316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:36.288799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:47.184874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:50.227715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:53.540225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:56.632537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:59.700771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:02.796535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:06.058676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:09.334792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:12.526024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:15.863595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:19.133578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:22.549459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:25.801593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:29.020664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:32.153782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:36.486616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:47.382442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:50.417978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:53.738730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:56.822626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:13:59.889974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:02.989360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:06.263047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:09.539561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:12.719632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:16.079121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:19.337802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:22.760096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:25.995117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:29.210668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:14:32.343278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-17T16:14:37.630113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-17T16:14:40.446774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3713pravidelnáYV2RT60A3LB3036852022-01-03D13K540 EUVIVOLVONÁKLADNÍ AUTOMOBILFHN32020-01-08244004způsobilé110001000000001195
3605pravidelnáSARRDWBKN4D6229082022-01-0318K4FMGOSOBNÍ AUTOMOBILMG TFM12003-12-11106264způsobilé150002001020007067
3618pravidelnáVF7CH6FZC390418292022-01-036FZCITROËNOSOBNÍ AUTOMOBILXSARA PICASSOM12001-08-02199227částečně způsobilé152001200110207928
3311pravidelnáWVWZZZ9NZ7Y1999442022-01-03BWBVWOSOBNÍ AUTOMOBILPOLOM12007-04-26237069způsobilé130001001010005835
3827pravidelnáVF32CHFXF419203692022-01-03HFXPEUGEOTOSOBNÍ AUTOMOBIL206M12001-10-15184648způsobilé120001000010007854
3748pravidelnáTMBGT61Z4B21210142022-01-03CAYCŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12011-03-01152059způsobilé110000001000004430
3219pravidelnáW0L0HAF687G0461762022-01-03Z12XEPOPELOSOBNÍ AUTOMOBILAGILAM12008-01-14165200způsobilé150001100120005572
3605pravidelnáVF7MFKFXB654390022022-01-03KFXCITROËNOSOBNÍ AUTOMOBILBERLINGOM12000-04-13360755způsobilé150002000120008404
3237pravidelnáTMBLF73T3E90092102022-01-03CFGBŠKODAOSOBNÍ AUTOMOBILSUPERBM12013-07-29272413způsobilé120000101000003549
3827pravidelnáYS2R4X200053732082022-01-03DC13 125SCANIANÁKLADNÍ AUTOMOBILR490N32015-01-07320595způsobilé120000000020003022
TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3609pravidelnáWVWZZZ1JZ3W6416602022-11-30AXRVOLKSWAGENOSOBNÍ AUTOMOBILGOLF VARIANTM12003-08-04321830způsobilé350001100120007196
3610pravidelnáVF36ERHRH217381652022-11-30RHRPEUGEOTOSOBNÍ AUTOMOBIL407M12008-11-12339383způsobilé370003001120005269
3610opakovanáTMBCS61Z9821664132022-11-30BXEŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12008-05-21141918způsobilé310000000010005444
3609opakovanáVF32CHFZE401197852022-11-30HFZPEUGEOTOSOBNÍ AUTOMOBIL206M11998-12-23105326způsobilé350001000130008881
3609opakovanáWF0NXXGCDN3J485082022-11-30FYDDFORDOSOBNÍ AUTOMOBILFOCUSM12003-10-01160993způsobilé320001000010007138
3609pravidelnáMMBJNK7402D0382382022-11-304D56MITSUBISHINÁKLADNÍ AUTOMOBILL 200N1G2002-06-04305721způsobilé370000121120007622
3849pravidelnáSHSRD87602U0288132022-11-30K20A4HONDAOSOBNÍ AUTOMOBILCRVM12003-02-13226502způsobilé330001001010007368
3609pravidelnáWVWZZZ1JZ2D3500062022-11-30ATDVWOSOBNÍ AUTOMOBILGOLF VARIANTM12002-02-13384127způsobilé350001010120007733
3609pravidelnáWF0GXXGBBGAL353072022-11-30QXBAFORDOSOBNÍ AUTOMOBILMONDEOM12010-05-26291165způsobilé340000001120004709
3609pravidelnáTMBEEF613W08510202022-11-30781.135 MŠKODAOSOBNÍ AUTOMOBILFELICIAM11999-03-08169654způsobilé370011101120008806

Duplicate rows

Most frequently occurring

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays# duplicates
358evidenčníRFCFBEVGA9Y0041602022-11-03TBGTGBMOTOCYKLTARGETLE2009-03-091448částečně způsobilé4010100000000051524
465evidenčníTMAJB811DMJ0642752022-11-15G4FTHYUNDAIOSOBNÍ AUTOMOBILTUSCONM12021-12-1010698částečně způsobilé201010000000004934
1729evidenčníVF3BERFNC860459322022-11-08RFNPEUGEOTOSOBNÍ AUTOMOBILEXPERTM12003-08-29211945částečně způsobilé2010100000000071714
3344evidenčníZCFC652C7055136142022-11-25F1CFL411BIVECONÁKLADNÍ AUTOMOBIL50C18N22022-11-09515částečně způsobilé502020000000001594
3700na žádost zákazníkaVF38EAHRMFL0332792022-11-02AH01PEUGEOTOSOBNÍ AUTOMOBIL508M12015-09-01158142částečně způsobilé3010100000000027854
9121opakovanáTSMEYB21S001913672022-11-14M16ASUZUKIOSOBNÍ AUTOMOBILSX4M12007-08-22121376částečně způsobilé1410020010200057174
42566pravidelnáTMBCS61Z0520143062022-11-03BJBŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12004-11-01357675způsobilé4600020020200067414
62592pravidelnáTMBJG7NE3H01046672022-11-26CXXŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12016-11-03129225způsobilé6200010000100023564
87411pravidelnáVF1FDC1L6402579262022-11-30G9U A 6RENAULTNÁKLADNÍ AUTOMOBILMASTERN12008-07-23502569částečně způsobilé3530020020310053814
96222pravidelnáVF33ENFUB824752452022-11-23NFUPEUGEOTOSOBNÍ AUTOMOBIL307M12002-06-07192806způsobilé3700030010300076194